Modifiable and non‐modifiable disparity‐related predictors of cognitive scores in a population‐based cohort: the ELSI‐Brazil
Bibliographic record
Abstract
Abstract Background Social and health‐related disparity factors are important predictors of brain health in low and middle‐income countries (LMIC). Social predictors of cognition have a higher impact on brain health among LMICs than classic demographic factors, such as age and sex. This study aimed to evaluate the impact of modifiable and non‐modifiable social and health‐related factors on cognition in a Brazilian population‐based cohort. Method We selected 9,412 individuals from the ELSI‐Brazil cohort, which is a population‐based study conducted with a similar design as the Health and Retirement Study. Complex sample design stratified and clustered distinct geographical regions according to their representation nationally. We included health‐related disparity factors (hypertension, diabetes, cardiovascular disease, and falls), as well as social determinants of health (piped water, access to healthcare, private health coverage, income, food insecurity, and education). Non‐modifiable factors were also retrieved (age, sex, race and region). We evaluated cognition using a global composite of orientation, animal fluency, and memory recall scores. A linear regression model was conducted to identify and stratify predictors of global cognitive scores (R2 adjusted = 0.336). Result We included 5,432 individuals above 60 years of age in this analysis (mean age 70.3±7.97 yo). The regression model identified that the most important modifiable predictor of cognition was depression (ß = ‐0.14±0.06, p < 0.001), followed by physical activity (ß = 0.12±0.03, p < 0.001), and private health coverage (ß = 0.09±0.06, p = 0.001). Among non‐modifiable predictors of cognition, northeast region dwelling was the most important predictor (ß = ‐0.16±0.05, p < 0.001), following residing in the north region (ß = ‐0.14±0.10, p = 0.006), and brown race (ß = ‐0.1±0.05, p < 0.001). No health‐related factor reached significance. Conclusion Overall, modifiable and non‐modifiable social‐related disparity factors had a significant impact on cognition in this study. Modifiable factors should be addressed in public health policies, while non‐modifiable risk factors may provide insights on compensatory strategies to overcome their negative effects on brain health. Further studies may expand this investigation to other LMIC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".